Speaker: Hanxi Fang

Title: Enhancing Computational Notebooks with Code+Data Space Versioning

Abstract:
There is a gap between how people explore data and how Jupyter-like computational notebooks are designed. People explore data nonlinearly, using execution undos, branching, and/or complete reverts, whereas notebooks are designed for sequential exploration. Recent works like ForkIt are still insufficient to support these multiple modes of nonlinear exploration in a unified way. In this work, we address the challenge by introducing two-dimensional code+data space versioning for computational notebooks and verifying its effectiveness using our prototype system, Kishuboard, which integrates with Jupyter. By adjusting code and data knobs, users of Kishuboard can intuitively manage the state of computational notebooks flexibly, thereby achieving both execution rollbacks and checkouts across complex multi-branch exploration history. Moreover, this two-dimensional versioning mechanism can easily be presented along with a friendly one-dimensional history. Human subject studies indicate that Kishuboard significantly enhances user productivity in various data science tasks.

Bio:
Hanxi Fang is a second-year M.S. student in Computer Science at the University of Illinois Urbana-Champaign advised by Prof. Yongjoo Park. Her research interest is mostly in database systems and systems for data science. During her master’s program, she mostly works on version control systems for data science and optimizing systems for efficient large language model (LLM) inference. Before her graduate studies, she worked as an undergraduate research assistant at the Database and Big Data Analysis Lab at Zhejiang University in China, where she worked in spatiotemporal databases.